A few weeks ago, a founder told me it took three hours of endless clicking to find an AI-generated portrait of a Black woman. It reminded me, in some ways, of a speech I saw three years ago when Yasmin Green, the then-director of research and development for Jigsaw, spoke about how human bias seeps into the programming of AI. Her talk and this founder, miles away and years apart, are two pieces of the same puzzle.
Discussions about diversity are more important than ever as AI enters a new golden era. Every new technology that appears seems to be accompanied by some harrowing consequence. So far, AI has contributed to racist job recruiting tactics and slower home approval rates for Black people. Self-driving cars have trouble detecting dark skin, making Black people more likely to be hit by them; in one instance, robots identified Black men as being criminals 9% more than they did white men, which would be put under a new light if judicial systems ever begin adopting AI.
AI ethics is often a separate conversation from AI building, but they should be one and the same. Bias is dangerous, especially as AI continues to spread into everyday life. For centuries, doctors once judged Black people on criteria now deemed racist, with one prevalent belief being that such people experienced less pain. Today, algorithms discriminate against Black people; one study from 2019 found that an algorithm used by U.S. hospitals “was less likely to refer Black people than white people who were equally sick to programs that aim to improve care for patients with complex medical needs.”
Right now, bias appears in various AI subsectors, ranging from investment to hiring to data and product execution, and each instance of bias props up others. Eghosa Omoigui, the founder of EchoVC Partners, told TechCrunch that though AI can be “incredibly powerful,” society is still far from “flawless” artificial intelligence.
“This means that the likelihood of AI bias in outcomes remains high because of the excessive dependencies on the sources, weights and biases of training data,” he said. “Diverse teams will prioritize the exquisite understanding and sensitivity necessary to deliver global impact.”
Omoigui’s brother, Nosa, the founder of ESG compliance regulator Weave.AI, reiterated that point. Many of these models are black boxes, and creators have no particular insights into the inner workings of how a prediction or recommendation is achieved, he said. Compared to Wall Street, AI is practically unregulated, and as the level of governance fails to match its growth, it risks going rogue. The EU proposed steps to reduce and account for bias in AI-powered products, with some pushback, though the proposition itself puts it slightly ahead of where the U.S. is now.
In fact, Eghosa said many investors don’t care or think about diversity at all within AI and that there is a groupthink mentality when it comes to machine-led capabilities. He recalled the reactions investors gave him when he helped lead an investment round for the software company KOSA AI, which monitors AI for bias and risks.
“Quite a few investors that we spoke to about the opportunity felt very strongly that AI bias wasn’t a thing or that a ‘woke product’ wouldn’t have product-market fit, which is surprising, to say the least,” Eghosa said.
It should be no surprise that investors don’t think bias is a problem, given that they show it themselves in their investment patterns, particularly in AI. Crunchbase data shows that in 2019, U.S.-based companies with at least one Black co-founder raised $40 million out of the total $19.95 billion in venture funds allocated to U.S.-based AI companies. U.S.-based companies with at least one Black co-founder picked up $308 million out of $41.57 billion in 2021 and $54 million out of $23.48 billion in 2022.
Data visualization by Miranda Halpern, created with Flourish
The capital amount allocated for companies with at least one Black co-founder has hovered under 1% for the past few years, with no clear indication that it is increasing anytime soon, compared to companies with at least one woman founder, for example, which have seen a year-over-year funding percentage increase since 2019.
The lack of investment in new companies with Black founders is aided by the lack of diversity in AI teams at existing companies, which, in turn, impacts product execution. All of this, combined with the lack of governance, creates a self-perpetuating cycle. Parfait, an AI-powered wig company, targets the expensive and time-consuming needs of managing Black hair. Its co-founder, Isoken Igbinedion, said investors were intrigued by her product, especially since using AI within that industry wasn’t common.
Calling AI a numbers game, Igbinedion said that if there isn’t enough robust representation of all types of people in datasets, then resulting applications won’t work well for everyone.
“This is especially frightening for Black women who are typically the most underrepresented,” she told TechCrunch. As a Black founder, she was able to create a product that could cater to her community as she understood its needs; such nuance is not something often seen or prioritized by the white men building these robust AI companies.
Igbinedion said solving the problem of AI bias requires more than the ability to bootstrap; there needs to be real support that comes with real dollars that can invest in long-term solutions. Once again, Black founders are willing to fill the gaps, but in this industry, there is not enough attention being paid as to why it is always diverse individuals who must stand up and ignite change.
Parfait is spending time working to construct datasets to help others build equitable training models — basically writing better recipes to feed machines. Nosa implemented a knowledge graph operated by an algorithm with human oversight to prevent his system from making any “random” suggestions.
“As AI pervades society, it is critical for developers and corporations to be good stewards of this technology but also hold fellow technologists accountable for these unethical use cases,” Igbinedion added.
While chatting with TechCrunch, Ekechi Nwokah, the founder of the embedded lending platform Migo, raised an interesting point. He said there is so much excitement in this space right now — people are building so fast and investors are so bullish — that there often isn’t any time to put in the work to look for a diverse team or to parse through algorithms to make everything perfectly unbiased or even just less biased than it is now. Especially in the early stages, entrepreneurs want to get their products out, he said, adding, though, that as a business matures, there is an increased likelihood that founders will backtrack to fix any problems.
“I think most folks that are working on AI models have good intent,” Nwokah told TechCrunch. He said he tries to prevent bias by focusing on hiring and implementing policies that have diversity as a core pillar of his company, hoping it then leads to better decision-making regarding how products are built.
At the same time, it takes time to fine-tune models and unweave the historical cause-and-effect input data given to these AI models, Nwokah continued. “AI models predict the future. It’s a very difficult thing to do, and you can only predict the future based on what’s happened in the past, so the only way the model is gonna predict is based on what it’s seen before, just like a human being,” Nwokah said.
Perhaps then, to combat the biased narratives fed to some AI models, the builders must stitch a representation of a new society, an ideal one. As Nwokah insinuated, the past has been written, and the builders of AI must envision and implement a better future.
Eghosa, meanwhile, said he worried that this generative AI “frenzy” is causing people to forget the requisite respect, recognition and guardrails that AI-infused tools and products should bear.
As in other areas of entrepreneurship with diverse founders, he said underrepresented founders already prioritize equitable building and execution. For some reason, other founders cannot master this multitasking, which is why it took three hours to find an AI-generated portrait of a Black woman.
The concept of the generative model was great, but the algorithm was trained from a mainstream photo-sharing site and inherited its biases. The photos on that popular site had many images of white women, meaning it was easier for the algorithm to create believable white faces than for other races. That system is a few years old, but the problems that produced it still plague the industry.
“I don’t know if we will ever completely get rid of bias,” Eghosa said. “That said, I am excited that there are — and I am confident more are on the way — potential scalable approaches powered by AI to identify it, analyze it and offer data-driven decision-support tools and platforms to mitigate or eliminate it. Its power and utility are widely underestimated. With requisite guardrails, it will empower and turbocharge decision-making, productivity and economic growth while creating more access to opportunities and a societal lift.”
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